Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data
| Autor: | Peter P. Lee, Grigoriy Gogoshin, Joseph Chao, Lei Wang, Andrei S. Rodin, Colt Egelston, Russell C. Rockne, Seth Michael Hilliard |
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| Rok vydání: | 2020 |
| Předmět: |
Systems biology
medicine.medical_treatment FACS Population Computational biology Adenocarcinoma Article Catalysis Flow cytometry Inorganic Chemistry lcsh:Chemistry Immune system Cancer immunotherapy Humans Medicine Physical and Theoretical Chemistry education Molecular Biology immuno-oncology lcsh:QH301-705.5 Spectroscopy Gastrointestinal Neoplasms education.field_of_study medicine.diagnostic_test business.industry flow cytometry Organic Chemistry Bayesian network Cancer General Medicine Cell sorting medicine.disease Immune checkpoint Computer Science Applications Blockade Bayesian networks machine learning lcsh:Biology (General) lcsh:QD1-999 gating Leukocytes Mononuclear immune networks Immunotherapy business |
| Zdroj: | International Journal of Molecular Sciences, Vol 22, Iss 2316, p 2316 (2021) International Journal of Molecular Sciences Volume 22 Issue 5 |
| DOI: | 10.1101/2020.06.14.151100 |
| Popis: | Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders and non-responders. Our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry (FACS, Fluorescence-activated cell sorting) data characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. Here, we describe a novel computational pipeline developed by us to perform secondary analyses of FACS data using systems biology / machine learning techniques and concepts. It is centered around comparative Bayesian network analyses of immune signaling networks and is capable of detecting true positive signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up immune markers (and combinations / interactions thereof) associated with clinical responses that were identified by this computational pipeline. |
| Databáze: | OpenAIRE |
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